Jurnal Ilmu Komputer dan Informasi最新文献_第4页

Facial Expression Recognition using Residual Convnet with Image Augmentations 基于图像增强的残差卷积神经网络面部表情识别

Jurnal Ilmu Komputer dan Informasi Pub Date : 2021-07-04 DOI: 10.21609/jiki.v14i2.968

Fadhil Yusuf Rahadika, N. Yudistira, Y. A. Sari

{"title":"Facial Expression Recognition using Residual Convnet with Image Augmentations","authors":"Fadhil Yusuf Rahadika, N. Yudistira, Y. A. Sari","doi":"10.21609/jiki.v14i2.968","DOIUrl":"https://doi.org/10.21609/jiki.v14i2.968","url":null,"abstract":"During the COVID-19 pandemic, many offline activities are turned into online activities via video meetings to prevent the spread of the COVID 19 virus. In the online video meeting, some micro-interactions are missing when compared to direct social interactions. The use of machines to assist facial expression recognition in online video meetings is expected to increase understanding of the interactions among users. Many studies have shown that CNN-based neural networks are quite effective and accurate in image classification. In this study, some open facial expression datasets were used to train CNN-based neural networks with a total number of training data of 342,497 images. This study gets the best results using ResNet-50 architecture with Mish activation function and Accuracy Booster Plus block. This architecture is trained using the Ranger and Gradient Centralization optimization method for 60000 steps with a batch size of 256. The best results from the training result in accuracy of AffectNet validation data of 0.5972, FERPlus validation data of 0.8636, FERPlus test data of 0.8488, and RAF-DB test data of 0.8879. From this study, the proposed method outperformed plain ResNet in all test scenarios without transfer learning, and there is a potential for better performance with the pre-training model. The code is available at https://github.com/yusufrahadika-facial-expressions-essay.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"182 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76045320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Student Academic Mark Clustering Analysis and Usability Scoring on Dashboard Development Using K-Means Algorithm and System Usability Scale 基于K-Means算法和系统可用性量表的学生学业成绩聚类分析与仪表板可用性评分

Jurnal Ilmu Komputer dan Informasi Pub Date : 2021-07-04 DOI: 10.21609/jiki.v14i2.980

N. Amalia, A. A. Supianto, N. Setiawan, Vicky Zilvan, A. R. Yuliani, A. Ramdan

{"title":"Student Academic Mark Clustering Analysis and Usability Scoring on Dashboard Development Using K-Means Algorithm and System Usability Scale","authors":"N. Amalia, A. A. Supianto, N. Setiawan, Vicky Zilvan, A. R. Yuliani, A. Ramdan","doi":"10.21609/jiki.v14i2.980","DOIUrl":"https://doi.org/10.21609/jiki.v14i2.980","url":null,"abstract":"Learning activities are one of the processes of delivering information or messages from teachers to students. SMPN 4 Sidoarjo is a State Junior High School (JHS) located in Sidoarjo Regency. During the learning process, the collected academic score data were still not well organized by teachers and school principals in monitoring student learning performance. The score data is from Bahasa Indonesia subject from a teacher with 222 data included at 2019/2020 school year. The method used in student clustering is K-Means. The number of clusters are determined using the elbow method and displayed in graphic form. Clustering result can be used as a reference for teachers in determining study groups and determining the best treatment for each cluster. The best clustering results are proven by validation score using Davies-Bouldin Index, Silhouette Width, and Calinski-Harabasz Index. Three clusters were obtained for each class level of data, while the cluster ranges from two to five for the data for each study group. The dashboard is used in order to visualize the clustering result. Usability testing using System Usability Scale (SUS) has a score value of 87.5, which means that the dashboard can be accepted by SMPN 4 Sidoarjo.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81419151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Brain Tumors Detection By Using Convolutional Neural Networks and Selection of Thresholds By Histogram Selection 基于卷积神经网络的脑肿瘤检测及直方图选择的阈值选择

Jurnal Ilmu Komputer dan Informasi Pub Date : 2021-07-04 DOI: 10.21609/JIKI.V14I2.859

Kasiful Aprianto

引用次数: 3

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review 使用机器学习技术的股票交易预测:一个全面和系统的文献综述

Jurnal Ilmu Komputer dan Informasi Pub Date : 2021-07-04 DOI: 10.21609/JIKI.V14I2.935

Rico Bayu Wiranata, A. Djunaidy

{"title":"The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review","authors":"Rico Bayu Wiranata, A. Djunaidy","doi":"10.21609/JIKI.V14I2.935","DOIUrl":"https://doi.org/10.21609/JIKI.V14I2.935","url":null,"abstract":"This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86840510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

COVIWD: COVID-19 Wikidata Dashboard

Jurnal Ilmu Komputer dan Informasi Pub Date : 2021-02-28 DOI: 10.21609/JIKI.V14I1.941

Fariz Darari

引用次数: 1

STUDENT ATTENDANCE SYSTEM USING WIFI DIRECT AND TEMPORARY WI-FI HOTSPOT 使用 WIFI 直播和临时 WIFI 热点的学生考勤系统

Jurnal Ilmu Komputer dan Informasi Pub Date : 2020-03-14 DOI: 10.21609/JIKI.V13I1.750

Doni S. Pambudi, Taufiqotul Bariyah

引用次数: 4

Analyzing Depthwise Convolution Based Neural Network: Study Case in Ship Detection and Land Cover Classification 基于深度卷积的神经网络分析——以船舶检测和土地覆盖分类为例

Jurnal Ilmu Komputer dan Informasi Pub Date : 2019-07-08 DOI: 10.21609/JIKI.V12I2.752

Kuntoro Adi Nugroho, Y. E. Windarto

引用次数: 0

MULTI-CLASS REGION MERGING FOR INTERACTIVE IMAGE SEGMENTATION USING HIERARCHICAL CLUSTERING ANALYSIS 基于层次聚类分析的多类区域合并交互式图像分割

Jurnal Ilmu Komputer dan Informasi Pub Date : 2019-07-08 DOI: 10.21609/JIKI.V12I2.757

K. Aisyah, Syadza Anggraini, N. N. Putriwijaya, A. Arifin, R. Indraswari, D. A. Navastara

引用次数: 0

APPLICATION OF THE FORWARD CHAINING METHOD IN DOWN SYNDROME PATIENTS 正链法在唐氏综合征患者中的应用

Jurnal Ilmu Komputer dan Informasi Pub Date : 2019-07-08 DOI: 10.21609/JIKI.V12I2.694

Kurniati

引用次数: 1

AN INTELLIGENT DENGUE HEMORRHAGIC FEVER SEVERITY LEVEL DETECTION BASED ON DEEP NEURAL NETWORK APPROACH 基于深度神经网络的登革出血热严重程度智能检测

Jurnal Ilmu Komputer dan Informasi Pub Date : 2019-07-08 DOI: 10.21609/JIKI.V12I2.642

Dian Pratiwi, Gatot Santoso, Leni Muslimah, Raden Davin Rizki

引用次数: 1